Optimal dispatching method of energy storage in distribution network based on dynamic load forecasting

Abstract

The present invention relates to the field of smart grid technology, specifically to a method for optimizing and dispatching energy storage in a distribution network based on dynamic load forecasting, comprising the following steps: obtaining power data to extract inversion nodes and calibrate disturbance sections, identifying charge trends to screen dispatch units and construct dispatch links, fusing voltage and current directions to identify intervention nodes to set release limits, and generating an energy storage release freeze instruction set. In the present invention, by extracting direction reversal points from power data of energy storage access nodes in the distribution network, constructing trend sequences in combination with charge state change trends, excluding continuous decline units, and achieving synchronous matching of energy storage trends and disturbance behaviors within the time axis, a storage path map is constructed through the joint screening of trend direction consistency and connectivity path conditions, thereby enhancing the structural combination capability of energy storage resources, fusing the voltage fluctuation main vector and the current release auxiliary vector, and achieving a significant improvement in the response capability of distribution network energy storage resources and the accuracy of path control.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a distribution network energy storage optimization scheduling method based on dynamic load prediction. Background Art

[0002] The field of smart grid technology involves the deep integration of power systems and information and communications systems, aiming to achieve digital, automated, and visual management of power systems. Core elements of this technology include real-time monitoring and intelligent control of the entire process of power generation, transmission, distribution, and consumption. It also enhances the flexibility and economic efficiency of grid operations through data collection, two-way communication, state awareness, load forecasting, and resource coordination. Smart grids systematically integrate distributed energy access, energy storage regulation, power market mechanisms, and user-side interactive responses to form an energy internet infrastructure that operates collaboratively across multiple entities, energy sources, and scenarios.

[0003] Among them, the distribution network energy storage optimization scheduling method based on dynamic load forecasting refers to a power dispatching method that combines load change trend analysis with energy storage operation. The patent subject mainly addresses the problems of strong load fluctuation and unstable matching of power supply and demand in the operation of the distribution network, covering load forecasting modeling, power time series distribution analysis and energy storage equipment charging and discharging strategy formulation. Its method is to obtain short-term dynamic load change trends by constructing a load forecasting model based on load data and time series characteristics, and then conduct power balance analysis and time-based energy storage plan formulation based on the forecast results. Specifically, mathematical optimization methods are used to allocate energy storage charging and discharging time series to meet the power regulation needs of the power grid and the local supply and demand balance requirements.

[0004] Although existing technologies can achieve short-term trend judgment and formulate energy storage plans through load forecasting models, they lack a node-level mutation identification mechanism when responding to sudden disturbances, which can easily lead to delayed or misjudgment of abnormal load responses. The identification of the state of charge remains at the mean or maximum capacity judgment within the cycle, and the state change trend is not used as the basis for scheduling optimization, making it difficult to dynamically respond to the current energy storage characteristics. In terms of energy storage path organization, fixed node weights are often used for modeling, ignoring the joint evaluation of trend consistency and structural connection capabilities. The path construction lacks systematicity and scalability. Voltage and current characteristics are only used as result monitoring indicators, and no active judgment parameter combination is formed, making it difficult to achieve intervention target screening before scheduling. In actual operation, problems such as energy storage response mismatch, continuous voltage fluctuations, and ineffective current release occur, reducing the flexible adjustment capability of the power grid and the response efficiency of energy storage resources. Summary of the Invention

[0005] In order to solve the problem that the existing technology lacks a node-level mutation identification mechanism when dealing with sudden disturbances, which easily leads to delayed or misjudgment of abnormal load response. The identification of the state of charge remains at the mean or maximum capacity judgment within the cycle, and the state change trend is not used as the basis for scheduling optimization, making it difficult to dynamically respond to the current energy storage characteristics. In terms of energy storage path organization, fixed node weights are mostly used for modeling, ignoring the joint evaluation of trend consistency and structural connection capabilities, and the path construction lacks systematicity and scalability. Voltage and current characteristics are only used as result monitoring indicators, and no active judgment parameter combination is formed, making it difficult to implement intervention target screening before scheduling. In actual operation, problems such as energy storage response mismatch, continuous voltage fluctuations, and ineffective current release occur, which reduce the technical problems of reducing the flexible adjustment capability of the power grid and the response efficiency of energy storage resources. The present invention provides a distribution network energy storage optimization scheduling method based on dynamic load forecasting. The technical solution is as follows:

[0006] On the one hand, a method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting is provided, the method comprising:

[0007] S1: Collect power data from energy storage access nodes in the distribution network over consecutive time slices, detect the power change direction in adjacent time slices, extract direction reversal points as candidate mutation nodes, compare and determine the mutation identification reference direction, calibrate the starting node, locate the reversal end node, and generate a load disturbance segment sequence.

[0008] S2: Based on the load disturbance segment sequence identification time window, extract the state of charge records of the distribution network energy storage access nodes, compare the state of charge change directions of adjacent cycles, determine whether the trend direction is continuously decreasing, and eliminate the continuously decreasing units to generate a list of energy storage units;

[0009] S3: Calling the energy storage unit list, mapping the charge trend sequence and the load disturbance segment sequence on the time axis, searching for unit combinations with path connection capabilities and consistent trend directions based on the connectivity relationship of the energy storage nodes in the distribution network, and constructing scheduling links in sequence according to the connection order between the nodes to generate an energy storage path map;

[0010] S4: Based on the energy storage path map, the fluctuation direction and the fluctuation rate are calculated to form a main vector, and the auxiliary vector is constructed by combining the current release direction and the charge state change direction of the energy storage unit to generate an intervention node trend set.

[0011] As a further solution of the present invention, the load disturbance segment sequence includes the disturbance starting point position, the disturbance ending point number, and the number of disturbance direction changes; the energy storage unit list includes the energy storage node number, charge trend direction, and the average charge within the cycle; the energy storage path map includes the node connectivity order, trend consistency identifier, and path scheduling priority sequence; the intervention node trend set includes the voltage change main vector, the current release auxiliary vector, and the node trend aggregation label.

[0012] As a further solution of the present invention, the steps of obtaining the load disturbance segment sequence are specifically as follows:

[0013] S101: Collect power data from consecutive time slices of energy storage access nodes in the distribution network, detect the power value between two adjacent time slices, and construct a power change direction sequence based on the difference in the power values. Select points where the change direction changes from positive to negative or from negative to positive as direction reversal points, extract them as mutation candidate nodes, and generate a mutation candidate node sequence.

[0014] S102: Based on the node position in the mutation candidate node sequence, the power values of the two time slices before and after the node are called, and the forward change amount and the backward change amount are respectively calculated. The two are compared with the node change direction, and the node whose change amplitude direction is consistent with the original change direction is determined as the starting node for identifying the reference direction, thereby obtaining a direction reference positioning node set;

[0015] S103: Locate the node set based on the direction reference, detect whether the power change direction of the adjacent nodes is continuously reversed, and locate the node as the disturbance end node if the number of continuous reversals exceeds the set number of reversals. Mark the time slice between the start node and the disturbance end node to obtain the load disturbance segment sequence.

[0016] As a further solution of the present invention, the steps of obtaining the energy storage unit list are specifically as follows:

[0017] S201: Extracting state of charge records of energy storage access nodes in the distribution network within a corresponding time period based on the intervals marked by the load disturbance segment sequence, arranging the state of charge values of the nodes in chronological order, and obtaining state of charge trend data of the energy storage nodes;

[0018] S202: Retrieving the state of charge values of adjacent cycles in the state of charge trend data of the energy storage node, sequentially comparing each set of values, recording change direction marks and connecting them to form a trend change sequence, identifying segments where the state of charge values continuously change, and obtaining a time slice set of the rising trend segment;

[0019] S203: Filter the energy storage access node numbers with an increasing state of charge trend according to the time position corresponding to the increasing trend segment time slice set, sort out the nodes that meet the requirements, remove duplicates and aggregate them, and generate an energy storage unit list.

[0020] As a further solution of the present invention, the steps of obtaining the energy storage path map are specifically as follows:

[0021] S301: Calling the energy storage unit list, aligning the corresponding charge trend sequence with the load disturbance segment sequence on the time axis, calculating the peak interval state value of the energy storage unit, screening energy storage units whose charge state shows an upward trend within the disturbance time slice and whose charge state value is within the peak interval within the node time period, and obtaining the peak interval trend energy storage node set;

[0022] S302: Based on the state of charge trend sequence of the nodes in the peak interval trend energy storage node set, count the number of time slices with continuous upward trends of the nodes, and number the nodes from most to least according to the duration of the trend, to obtain a trend-sorted energy storage node sequence;

[0023] S303: Sort each pair of nodes in the energy storage node sequence according to the trend, search for node pairs with path connection relationships in the distribution network structure, determine whether the trend directions of the nodes on the connection paths are consistent, and if consistent, arrange them in sequence according to the connection order in the network path to generate an energy storage path map.

[0024] As a further solution of the present invention, the peak interval state value of the energy storage unit is calculated using the formula:

[0025] ;

[0026] in, Represents the state value of the energy storage unit in the peak range, Representative The state of charge value of each time slice, Represents the state of charge value of the previous time slice, represents the time interval, Representative The load disturbance value of the time slice, is the minimum value, is the total number of time slices.

[0027] As a further solution of the present invention, the step of obtaining the intervention node trend set is specifically as follows:

[0028] S401: Based on the timing information corresponding to the node paths in the energy storage path map and the real-time voltage values of the nodes in the time period, the ratio of the voltage value difference in consecutive time slices to the time interval is calculated, and the ratio is combined with the voltage change direction of the adjacent time periods to construct a main vector to obtain a node main vector set;

[0029] S402: Based on the current release direction and charge state change direction data of the nodes in the node main vector set within the same time period, an auxiliary vector is constructed to screen nodes that simultaneously meet the requirements of voltage rate being in an increasing state, voltage value being in a continuously decreasing section, and current continuous release direction, to generate an intervention node trend set.

[0030] As a further solution of the present invention, the ratio of the voltage value difference to the time interval in consecutive time slices is calculated using the formula:

[0031] ;

[0032] in, Representative Node With node The ratio of the voltage difference within the time period to the time interval, and Represents nodes and nodes At the time point and The voltage value, Representative Node With node The time interval between Representative The direction of voltage change within a period of time, Represents the total number of voltage change directions.

[0033] As a further solution of the present invention, the method further includes step S5:

[0034] S5: calling the intervention node trend set, setting release change limits according to the release curve adjustment standard, performing amplitude limiting processing on the current release rate, and performing a scheduling behavior direction locking operation on the energy storage units in the energy storage path that are connected to the node, thereby generating an energy storage release freeze instruction set;

[0035] The energy storage release freezing instruction set includes a release rate limit value, a path direction locking number, and a freezing response instruction code.

[0036] As a further solution of the present invention, the steps for obtaining the energy storage release and freeze instruction set are specifically as follows:

[0037] S501: Based on the intervention node trend set, extract the release rate change value of the node within the time period, call the release change limit interval in the release curve adjustment setting, compare the node change value with the upper and lower limits of the interval, analyze the adjustment range of the release curve by determining whether it exceeds the upper and lower limits, and generate a release change determination trend value;

[0038] S502: Based on the release change determination trend value, a real-time node release current rate is limited, a release amplitude parameter corresponding to the rate value is extracted, and an interval clipping operation is performed with the boundary value of the release change limit interval, and the release current rate value is updated to generate a node release limit rate;

[0039] S503: Call the node release limit rate, match the energy storage unit in the energy storage path that is connected to the real-time intervention node, compare the scheduling direction value of the energy storage unit with the direction parameter in the constraint release rate set, lock the scheduling direction and write the direction value into the freeze instruction field, and generate an energy storage release freeze instruction set.

[0040] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0041] By extracting direction reversal points from power data at energy storage access nodes in the distribution network and locating load disturbance boundaries based on amplitude judgment and reversal continuity, this approach enables boundary identification and dynamic modeling of sudden load changes, improving the accuracy of disturbance response location. A trend sequence is constructed based on the state of charge (SOC) trend, excluding continuously decreasing units and retaining only rising trend segments as scheduling targets. This allows for synchronous matching of energy storage trends and disturbance behavior within the timeline. By jointly screening trend direction consistency and connectivity path conditions, a storage path map is constructed, enhancing the structural combination capabilities of energy storage resources. By integrating the main voltage fluctuation vector and the auxiliary current release vector, energy storage nodes that simultaneously meet these three characteristic conditions are identified, further enabling the selection of key locations for regulation. Combining release rate limits with a behavior direction locking mechanism, behavior boundaries and intensity regulation standards within the path are defined. Trend behavior is used as a clue to connect the energy storage state, power disturbance, voltage feedback, and scheduling links. A coordinated regulation mechanism is constructed based on dynamic behavior evolution, significantly improving the responsiveness of distribution network energy storage resources and the accuracy of path regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the workflow of the present invention; DETAILED DESCRIPTION

[0043] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0044] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0045] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0046] See also Figure 1The embodiment of the present invention provides a method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting. The processing flow of the method may include the following steps:

[0047] S1: Collect power data from energy storage access nodes in the distribution network over consecutive time slices, detect the direction of power changes in adjacent time slices, extract direction reversal points as candidate mutation nodes, call the power change amplitudes in the time slices before and after the candidate nodes, compare and determine the mutation identification reference direction, and then calibrate the starting node. Detect whether the change direction has continuous reversals, locate the reversal end node, and generate a load disturbance segment sequence.

[0048] S2: Based on the load disturbance segment sequence identification time window, the state of charge records of the distribution network energy storage access nodes are extracted. The state of charge change directions of adjacent cycles are compared and a trend sequence is formed. The trend direction is determined to be continuously decreasing and the continuously decreasing units are eliminated. The units with an increasing trend are retained as the scheduling objects, and a list of energy storage units is generated.

[0049] S3: Call the energy storage unit list, map the charge trend sequence with the load disturbance segment sequence on the time axis, screen the energy storage units that show an upward trend in the disturbance segment and whose charge state is in the peak segment, mark them with priority according to the trend persistence, and search for unit combinations with path connection capabilities and consistent trend directions based on the connectivity of energy storage nodes in the distribution network. Build scheduling links in the order of node connection to generate an energy storage path map.

[0050] S4: Based on the energy storage path map and combined with real-time voltage change data, the fluctuation direction and fluctuation rate are calculated to form a main vector. The auxiliary vector is constructed by combining the current release direction and state of charge change direction of the energy storage unit. Nodes that meet the conditions of voltage rate increase, continuous voltage drop, and continuous current release are identified, and an intervention node trend set is generated.

[0051] S5: Call the intervention node trend set, set the release change limit according to the release curve adjustment standard, perform amplitude limiting on the current release rate, and perform scheduling behavior direction locking operation on the energy storage units connected to the node in the energy storage path, generating an energy storage release freeze instruction set;

[0052] The load disturbance segment sequence includes the disturbance starting point position, the disturbance ending point number, and the number of disturbance direction changes. The energy storage unit list includes the energy storage node number, charge trend direction, and the average charge value within the cycle. The energy storage path map includes the node connectivity order, trend consistency identifier, and path scheduling priority sequence. The intervention node trend set includes the voltage change main vector, the current release auxiliary vector, and the node trend aggregation label. The energy storage release freeze instruction set includes the release rate limit value, the path direction lock number, and the freeze response instruction code.

[0053] The specific steps for obtaining the load disturbance segment sequence are as follows:

[0054] S101: Collect power data from consecutive time slices of energy storage access nodes in the distribution network, detect the power value between two adjacent time slices, and construct a power change direction sequence based on the difference in the power values. Select points where the change direction changes from positive to negative or from negative to positive as direction reversal points, extract them as mutation candidate nodes, and generate a mutation candidate node sequence.

[0055] With the help of automated monitoring, such as distribution automation terminals (DTUs) or edge computing units, node power is sampled and recorded in real time, and the data is structured and stored at fixed intervals. The sampling interval is set to every 5 minutes, and the power of a node within 24 hours is recorded as 288 consecutive time slices to form a complete power time series. The power values of adjacent time slices are extracted in sequence and the difference is judged. Starting from the first time slice, the power change between each two consecutive time slices is calculated one by one. For each pair of time slices, the 10th and 11th slices are set, and their power values are read respectively. The size and sign of the difference are calculated to determine whether it is rising (positive). , decreasing (negative) or remaining flat (zero), continuously traverse the entire power sequence in this way, record the power change direction at each time point, generate a direction sequence representing the change trend, identify the node positions where the previous time slice is positive and the next time slice is negative, or the previous time slice is negative and the next time slice is positive in the generated direction sequence, and extract the node positions where such change directions turn. For example, if the power of a node increases from 2kW to 2.5kW at 2 pm and drops again to 2.2kW at 2:05, then 2:05 is an obvious power change reversal point, which is used as a mutation candidate node, and summarized one by one to obtain a mutation candidate node sequence.

[0056] S102: Based on the node position in the mutation candidate node sequence, the power values of the two time slices before and after the node are called, and the forward change amount and the backward change amount are respectively calculated. The two are compared with the node change direction, and the node whose change amplitude direction is consistent with the original change direction is determined as the starting node for identifying the reference direction, thereby obtaining the direction reference positioning node set;

[0057] The power values of the two time slices before and after each candidate node are called in turn, and the difference with the current node power is calculated respectively to form the forward change amount and the backward change amount to compare whether the change trend is consistent. The power P(t) of each candidate node at time t, as well as the power P(t-1) before time t and the power P(t+1) after time t are read. The difference between P(t) and the values on both sides is calculated respectively, and the direction of change is determined based on the positive or negative difference. If it is found that the forward and backward powers of the node are both increasing or decreasing, it means that the direction of change at the node is consistent. The direction has continuity characteristics and can be further identified as the starting reference point for direction identification. For example, the power of a certain energy storage node is 4.0kW at 3 pm, 3.5kW at 2:55 pm, and 4.3kW at 3:05 pm. It is found that both the forward and backward directions are positive changes. This node is judged to be a key starting node in a stable upward trend. Similar analysis is performed on the entire mutation candidate sequence to determine the consistency of its change trend node by node. Nodes with discontinuous change directions or excessive fluctuations are eliminated, and a set of representative direction reference positioning nodes is summarized and screened.

[0058] S103: Locate the node set based on the direction reference, detect whether the power change direction of the adjacent nodes is continuously reversed, and locate the node as the disturbance end node if the number of continuous reversals exceeds the set number of reversals. Mark the time slice between the start node and the disturbance end node to obtain the load disturbance segment sequence;

[0059] The power change direction between any two adjacent nodes is continuously judged, mainly to check whether there is a frequent reversal of the power direction. The direction reference nodes are traversed one by one, starting from the first node, and the power change trends of each pair of adjacent nodes are detected backward to see if they are opposite. If the number of times the direction is continuously alternating reaches or exceeds the preset number threshold, such as 3 times, it is considered that the time at the current node no longer continues the initial disturbance trend, and it is marked as the disturbance end node. The time slice between the starting node and the end node will be combined to mark it as a complete disturbance segment. If it starts at 1 pm, The initial power increases continuously and turns to decrease at 1:20, increases again at 1:25, and decreases again at 1:30, which can be recorded as three consecutive direction reversals. Combined with the set reversal threshold of 3 times, 1:30 is determined to be the end point of the disturbance. The power records from the starting time of 1:00 to the ending time of 1:30 will be uniformly identified as a disturbance segment. During the whole process, the maximum allowable interval between each reversal can also be set. For example, the time difference between reversal points shall not exceed 10 minutes to avoid misidentifying fluctuations with too large intervals as continuous disturbances, ensure the accuracy and continuity of the disturbance segment division, and obtain the load disturbance segment sequence.

[0060] The steps for obtaining the energy storage unit list are as follows:

[0061] S201: Based on the intervals marked by the load disturbance segment sequence, extract the state of charge records of the energy storage access nodes in the distribution network within the corresponding time period, arrange the state of charge values of the nodes in chronological order, and obtain state of charge trend data of the energy storage nodes;

[0062] It is necessary to extract the state of charge data within the corresponding time period of the section from the real-time operation record of the energy storage access node, and retrieve the time boundary information within the disturbance section. If a disturbance section is defined as 8:00 to 8:45, it is necessary to query the SOC (State of The SOC data of each node is then sorted in ascending order by timestamp to form a time-indexed SOC time series. The SOC value at each time point constitutes the energy storage data of the node. For example, if energy storage node A records an SOC of 40%, 42%, and 43% at 8:00, 8:05, and 8:10, respectively, the SOC trend of this node within the disturbance section is 40%-42%-43%. Throughout the process, the node's time slices must be processed synchronously, and the continuity of the trend sequence must not be disrupted by missing values. Any data gaps can be repaired through linear interpolation or time window completion. After the disturbance section and the corresponding node are processed, the data of each node within each disturbance section is stored independently, and the corresponding node number and time period number are identified to obtain the SOC trend data of the energy storage node.

[0063] S202: Retrieving the SOC values of adjacent cycles in the SOC trend data of the energy storage node, comparing each set of values in sequence, recording the change direction marks and connecting them to form a trend change sequence, identifying the segments where the SOC values change continuously, and obtaining a set of time slices of the rising trend segment;

[0064] Read the SOC values of adjacent periods in each trend sequence one by one, and perform a sequential comparison operation on each pair of values. Read the SOC values of every two consecutive time slices, set t1 to 41% and t2 to 42%, judge that the change direction is rising, and record it as "+1". If t2 is lower than t1, record it as "-1", and if they are equal, record it as "0". Such marks are spliced one by one to form a trend change sequence to reflect the change pattern of SOC during the disturbance. Set the SOC change of node B in a certain disturbance segment to 41%-42%-43%-42%, then its trend change sequence is "+1, +1, -1", traverse the trend change sequence and identify the segments with consecutive "+1", which means that the SOC of the node is in a continuous rising state. The time index corresponding to the segment will be extracted as the rising trend segment time slice set. In order to improve the recognition accuracy, a minimum length threshold of continuous segments can be set. For example, the continuous rising time must not be less than two cycles (that is, at least 10 minutes in a row) to filter out false rises caused by single charging fluctuations. Set, if the SOC of node C is 45%, 46%, 47%, and 48% between 8:00 and 8:30, then this segment can be regarded as an rising trend segment, and the time points 8:00, 8:05, 8:10, and 8:15 are added to the time slice set for subsequent node screening and judgment to obtain the rising trend segment time slice set.

[0065] S203: Filter the energy storage access node numbers with an increasing state of charge trend based on the time positions corresponding to the increasing trend segment time slices, sort the nodes that meet the requirements, remove duplicates, and generate an energy storage unit list;

[0066] The energy storage access node numbers that show a continuous upward trend in state of charge within the segment are screened out, a mapping relationship between time slices and nodes is established, and nodes that show continuous charging behavior in each upward trend period are marked. The time slice set is traversed, and the corresponding energy storage nodes with a complete upward sequence are retrieved to confirm that they have continuous charging behavior throughout the disturbance period, and the node numbers are added to the candidate list. To avoid redundant records, the energy storage node numbers will be deduplicated after completing the segment analysis to ensure that each node appears only once in the list. If node D shows an upward SOC trend at multiple time points in the disturbance segment from 9:00 to 9:20 am, and it is verified that it meets the upward judgment criteria, the node D number will be recorded and output in a unified format according to the node number for subsequent control strategies, charge and discharge scheduling, or energy optimization. This ensures the integrity and operability of the energy storage unit identification process and generates an energy storage unit list.

[0067] The specific steps for obtaining the energy storage path map are as follows:

[0068] S301: Call the energy storage unit list, match the corresponding charge trend sequence with the load disturbance segment sequence on the time axis, calculate the peak interval state value of the energy storage unit, select the energy storage units whose charge state shows an upward trend in the disturbance time slice and whose charge state value is in the peak interval of the node time period, and obtain the peak interval trend energy storage node set;

[0069] Calculate the peak interval state value of the energy storage unit using the formula:

[0070] ;

[0071] in, Represents the state value of the energy storage unit in the peak range, Representative The state of charge value of each time slice, Represents the state of charge value of the previous time slice, represents the time interval, Representative The load disturbance value of the time slice, is the minimum value, is the total number of time slices;

[0072] Parameter meaning and formula calculation derivation process:

[0073] State of charge change :Indicates the The absolute value of the difference between the state of charge value of a time slice and the previous time slice;

[0074] Acquisition method: By real-time monitoring of the state of charge of the energy storage unit, the state of charge value of each time slice is obtained. ;

[0075] Set in The state of charge value of each time slice is , the state of charge value of the previous time slice is , then the change in state of charge is: ;

[0076] Time interval : Indicates the time interval between two consecutive time slices;

[0077] Acquisition method: Determine the time interval based on the sampling frequency. If sampling is performed once per minute, the time interval is 1 minute. minute

[0078] Load disturbance value :Indicates the Load disturbance value of a time slice;

[0079] Acquisition method: By real-time monitoring of the load changes of the distribution network, the load disturbance value of each time slice is obtained. ;

[0080] Set in The load disturbance value of a time slice is ;

[0081] Prevent division by zero for very small values : represents a very small constant used to prevent division by zero in calculations;

[0082] How to obtain: Set it to a very small constant, such as ;

[0083] Total number of time slices : Indicates the total number of time slices in the time series;

[0084] Acquisition method: Determine the total number of time slices based on the monitoring time range and time interval. If the monitoring time is 24 hours and sampling is once per minute, the total number of time slices is: ;

[0085] Set a sampling time period to once per minute, with a total of 5 time slices. The state of charge value and load disturbance value are shown in the following table:

[0086] Table 1 State of charge value and load disturbance value data

[0087] ;

[0088] As shown in Table 1, the state of charge value and load disturbance value of each time slice are given;

[0089] Based on the above data, calculate the charge state change, time interval and load disturbance value of each time slice:

[0090] Time Slice ;

[0091] Time Slice ;

[0092] Time Slice ;

[0093] Time Slice ;

[0094] Time Slice ;

[0095] Substitute the above data into the formula to calculate the contribution value of each time slice:

[0096] Time slice 1:

[0097] ;

[0098] Time slice 2:

[0099] ;

[0100] Time slice 3:

[0101] ;

[0102] Time slice 4:

[0103] ;

[0104] Time slice 5:

[0105] ;

[0106] Add up the contributions of all time slices:

[0107] ;

[0108] The results show that the state value of the energy storage unit in the peak interval is 0.5706, indicating that during this time period, the charge state change trend of the energy storage unit is relatively obvious, and the impact of load disturbance is small, which is suitable for energy storage optimization scheduling.

[0109] S302: Based on the state of charge trend sequence of the nodes in the peak interval trend energy storage node set, count the number of time slices with continuous upward trends of the nodes, and number the nodes from most to least according to the duration of the trend to obtain a trend-sorted energy storage node sequence;

[0110] Further analyze the state of charge trend sequence of each node, count the number of time slices covered by the continuous rising trend, traverse the trend sequence of each node, record the start and end positions of the continuous rising segment, and count the total number of time slices marked as "rising" continuously. If a node has 4 time slices showing continuous rising between 10:00 and 10:30, it is considered that its trend duration is 4 slices. After completing the node statistics, each node will be sorted according to its rising trend duration. The sorting rule is that the longer the duration, the higher the ranking, and the node sorting mark is assigned in a numbered manner. Note that if node B rises for 6 consecutive time slices and node C rises for 4 consecutive time slices, node B is numbered 1 and node C is numbered 2, and so on. After the sorting is completed, the number, node ID, trend length and other information of each node will be structured into a record for further network analysis. To ensure the fairness of the sorting, the time slice interval and continuity judgment criteria of each node are unified before sorting. It is set that at least two consecutive time slices are included in the trend length to exclude short-term jitter or discontinuous rise, ensure the stability and reliability of the statistical results, and obtain the trend-sorted energy storage node sequence.

[0111] S303: sort each pair of nodes in the energy storage node sequence according to the trend, search for node pairs with path connection relationships in the distribution network structure, determine whether the trend directions of the nodes on the connection path are consistent, and if consistent, arrange them in sequence according to the connection order in the network path to generate an energy storage path map;

[0112] Through the distribution network GIS or master station SCADA, the physical connection relationship of each energy storage node in the network is retrieved, and the path information of whether there is a direct or indirect electrical connection between the nodes is established. For the node pairs with path connections, the intermediate nodes included in the connection path are retrieved one by one to obtain the SOC trend direction information of the nodes in the disturbance section. The judgment standard is whether the charge state change direction of the node on the path in the corresponding time slice is consistent with the starting and ending nodes of the path. If the starting point and the end point both show an upward trend, and each node in the path also shows an SOC upward trend in the corresponding time slice, then the path is considered to be The paths have consistent trends. In this case, the node numbers are arranged in the order of the power grid paths to form a trend-consistent path chain. For example, if nodes 1, 2, 3, and 4 form a continuous path in physical connection, and these four nodes all show a continuous upward trend in SOC between 10:00 and 10:30, the recorded path is "1→2→3→4". As a trend link, the entire map consists of multiple trend-consistent paths. Each path represents the energy transfer or charging synchronization chain formed by the energy storage node during the disturbance period, providing a structural basis for subsequent scheduling or coordinated control strategies, and generating an energy storage path map.

[0113] The specific steps for obtaining the intervention node trend set are:

[0114] S401: Based on the timing information corresponding to the node paths in the energy storage path map and the real-time voltage values of the nodes within the time period, the ratio of the voltage value difference in consecutive time slices to the time interval is calculated. This is combined with the voltage change direction of adjacent time periods to construct a main vector and obtain a node main vector set.

[0115] Calculate the ratio of the voltage difference to the time interval in consecutive time slices using the formula:

[0116] ;

[0117] in, Representative Node With node The ratio of the voltage difference within the time period to the time interval, and Represents nodes and nodes At the time point and The voltage value, Representative Node With node The time interval between Representative The direction of voltage change within a period of time, Represents the total number of voltage change directions;

[0118] Parameter meaning and formula calculation derivation process:

[0119] Representative Node and nodes The ratio of the voltage difference between nodes to the time interval, in volts per second. This parameter is obtained by monitoring the voltage changes of nodes in different time periods.

[0120] and Represents nodes and nodes At the moment and The voltage value, in volts, is obtained through a voltage sensor in real-time monitoring;

[0121] Representative Node and nodes The time interval between the two time points is in seconds and is obtained by synchronizing the clocks to record the difference between the two time points.

[0122] Representative The direction of voltage change during the period is dimensionless. The direction of voltage change is determined by calculating the sign of the voltage change during the period. If the voltage changes from positive to negative, then , otherwise ;

[0123] is the number of time periods, representing the total number of voltage change directions, in units of units. This value is quantified based on the analyzed time window and actual monitoring data;

[0124] Obtaining specific values:

[0125] , the voltage value is read by the voltage sensor;

[0126] Seconds, the time interval is recorded by the synchronized clock system;

[0127] , set and select 3 time periods to calculate the voltage change direction;

[0128] , calculated based on the direction of voltage change in different time periods;

[0129] Formula calculation derivation:

[0130] Calculate the voltage difference:

[0131] ;

[0132] Calculate the time interval: ;

[0133] Calculate the voltage change direction and weighted sum:

[0134] ;

[0135] Substitute into the formula for calculation:

[0136] ;

[0137] This result shows that the node With node The voltage change rate between them is 0.48 volts per second, which means that within 5 seconds, the voltage difference changes at a rate of 0.48 volts per second relative to the time interval. This value is the key calculation result of this step and will be used as the basis for constructing the node main vector set later.

[0138] S402: Based on the current release direction and state of charge change direction data of the nodes in the node main vector set within the same time period, an auxiliary vector is constructed to select nodes that simultaneously meet the conditions of voltage rate growth, voltage value continuously decreasing, and current continuous release direction, and generate an intervention node trend set;

[0139] Analyze the current release direction and charge state change direction of the node in the same time period, and use this to construct an auxiliary vector. Extract the current flow direction data of the node in the time period corresponding to the main vector, and judge whether the current release direction is outward (discharge) or inward (charge). Combined with the change trend of the state of charge (SOC), set the SOC reduction to indicate that electric energy is being released. Integrate the voltage change direction, current release direction and SOC change trend into an auxiliary vector, in which the voltage needs to be continuously in the decreasing section, the voltage change rate is in an increasing state (that is, the decrease rate is accelerated or maintained), the current release direction remains consistent, and when the SOC shows a downward trend, the node is considered to meet the intervention conditions. If node B is between 10:00 and 10 :30 The voltage drops from 222V to 216V, and the rate of change gradually accelerates; the current always flows to the load end and the value is released stably, and the SOC drops from 90% to 75%. Node B is considered to have typical intervention trend characteristics, and its auxiliary vector is recorded as (↓acceleration, outflow, SOC drop). The node auxiliary vector set is traversed to screen nodes that meet the above three conditions at the same time. To avoid misjudgment, judgment criteria such as voltage drop rate thresholds such as 0.2V / min and current release direction duration thresholds such as 15 minutes can be set to ensure that the selected nodes have data sufficiency and behavioral consistency in intervention judgment, which facilitates the precise positioning of subsequent control strategies and the input of scheduling strategies, and generates an intervention node trend set.

[0140] The specific steps for obtaining the energy storage release freeze instruction set are:

[0141] S501: Based on the intervention node trend set, extract the release rate change value of the node within the time period, call the release change limit interval in the release curve adjustment setting, compare the node change value with the upper and lower limits of the interval, analyze the adjustment range of the release curve by determining whether it exceeds the upper and lower limits, and generate a release change determination trend value;

[0142] Conduct an in-depth analysis of the release behavior of the intervention node within a specific period of time, extract the release rate change value, read the current release rate of the adjacent time slice from the node operation data, calculate the rate difference between the current time slice and the previous time slice, and form a release rate change sequence. If the release rate of the node is 15A / min at 10:00 and 18A / min at 10:05, the release rate change is +3A / min, indicating that the release rate is enhanced. Recall the pre-set release curve to adjust the setting parameters. The setting is defined by the engineer based on the equipment carrying capacity and operation safety, including a release rate change limit range, set [-5A / min, +5A / min], compare the rate change value of each node with the upper and lower boundaries of the limit interval to determine whether it exceeds the boundary range. If the release rate change exceeds the upper limit of +5A / min, it is considered to be too fast release; if it is lower than the lower limit of -5A / min, it is considered to be too low release rate or reverse recovery. Based on this, a release change determination trend value is generated for each intervention node and marked with symbols, such as "+" indicates exceeding the upper limit, "-" indicates below the lower limit, and "0" indicates that the change is within the interval. The change determination trend value of the intervention node forms structured output data, which is used in the next step of limiting processing to generate a release change determination trend value.

[0143] S502: Based on the release change trend value, the real-time node release current rate is limited, the release amplitude parameter corresponding to the rate value is extracted, and the interval is clipped with the boundary value of the release change limit interval, the release current rate value is updated, and the node release limit rate is generated;

[0144] The current real-time current release rate is clipped. This process uses the release rate as the underlying data and limits its variation to ensure operational safety. The difference between the actual release rate of each node in the current time slice and the rate of the previous time slice is identified and extracted as the "release amplitude parameter." This parameter is compared with the release variation limit interval, and the interval is clipped to ensure that the change rate does not exceed the set boundary. For example, if a node's release rate increases from 20A / min to 28A / min, with a release amplitude of +8A / min, exceeding the set upper limit of +5A / min, it is updated to 25A / min, forcing the rate to remain within the allowable range. If the release rate decreases from 14A / min to 10A / min, with a change of -4A / min, within the allowable range, no adjustment is made. The clipped current release rate is then updated and written into the current node's operating parameters, used to control the node's release behavior during the current period, ensuring that stability is not affected by sudden rate changes. The entire process runs independently for each node, retaining both the original and adjusted rates for subsequent scheduling or evaluation analysis to form the node's release rate limit.

[0145] S503: Invoke the node release limit rate, match the energy storage unit in the energy storage path that is connected to the real-time intervention node, compare the scheduling direction value of the energy storage unit with the direction parameter in the constraint release rate set, lock the scheduling direction and write the direction value into the freeze instruction field, and generate the energy storage release freeze instruction set;

[0146] The system then associates the energy storage path graph with the energy storage units that are directly or indirectly connected to the intervention node. The path structure graph is then called to identify the energy storage units in the same path as the intervention node. The system then extracts their current dispatch direction value (i.e., the charge / discharge direction), labeled "in" or "out." This direction value is then compared with the direction parameter in the node's release limit rate to determine if they are consistent. Setting the limit rate direction to release (out) and the dispatch direction value to "out" confirms consistency. If the directions are inconsistent, a dispatch direction deviation exists, requiring correction or freezing of the dispatch instructions. For consistent nodes, their dispatch directions are written into the "freeze instruction field" to ensure that the energy storage unit maintains its current direction within the current dispatch cycle, avoiding instability caused by frequent switching. The freeze instruction field corresponds one-to-one to each node in the energy storage path and contains parameter information such as the node number, freeze direction, and upper and lower rate limits. This provides a constraint basis for the subsequent execution layer to issue dispatch commands, ensuring continuity and consistency in dispatch behavior during the disturbance response period, thus forming a set of energy storage release freeze instructions.

[0147] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A distribution network energy storage optimization scheduling method based on dynamic load forecasting, characterized in that: The following steps are involved: S1: Collect power data from energy storage access nodes in the distribution network over consecutive time slices, detect the power change direction in adjacent time slices, extract direction reversal points as candidate mutation nodes, compare and determine the mutation identification reference direction, calibrate the starting node, locate the reversal end node, and generate a load disturbance segment sequence. S2: Based on the load disturbance segment sequence identification time window, extract the state of charge records of the distribution network energy storage access nodes, compare the state of charge change directions of adjacent cycles, determine whether the trend direction is continuously decreasing, and eliminate the continuously decreasing units to generate a list of energy storage units; S3: Calling the energy storage unit list, mapping the charge trend sequence and the load disturbance segment sequence on the time axis, searching for unit combinations with path connection capabilities and consistent trend directions based on the connectivity relationship of the energy storage nodes in the distribution network, and constructing scheduling links in sequence according to the connection order between the nodes to generate an energy storage path map; S4: Based on the energy storage path map, the fluctuation direction and the fluctuation rate are calculated to form a main vector, and the auxiliary vector is constructed by combining the energy storage unit current release direction and the charge state change direction to generate an intervention node trend set; The steps for obtaining the energy storage path map are specifically as follows: S301: Calling the energy storage unit list, aligning the corresponding charge trend sequence with the load disturbance segment sequence on the time axis, calculating the peak interval state value of the energy storage unit, screening energy storage units whose charge state shows an upward trend within the disturbance time slice and whose charge state value is within the peak interval within the node time period, and obtaining the peak interval trend energy storage node set; S302: Based on the state of charge trend sequence of the nodes in the peak interval trend energy storage node set, count the number of time slices with continuous upward trends of the nodes, and number the nodes from most to least according to the duration of the trend, to obtain a trend-sorted energy storage node sequence; S303: sorting each pair of nodes in the energy storage node sequence according to the trend, searching for node pairs with path connection relationships in the distribution network structure, determining whether the trend directions of the nodes on the connection paths are consistent, and if consistent, arranging them in sequence according to the connection order in the network path to generate an energy storage path map; The energy storage unit peak interval state value is calculated using the formula: ； in, Represents the state value of the energy storage unit in the peak range, represents the state of charge value of the i-th time slice, Represents the state of charge value of the previous time slice, represents the time interval, represents the load disturbance value of the i-th time slice, is the minimum value, is the total number of time slices.

2. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 1, characterized in that: The load disturbance segment sequence includes the disturbance starting point position, the disturbance ending point number, and the number of disturbance direction changes; the energy storage unit list includes the energy storage node number, charge trend direction, and the average charge value within the cycle; the energy storage path map includes the node connectivity order, trend consistency identifier, and path scheduling priority sequence; the intervention node trend set includes the voltage change main vector, the current release auxiliary vector, and the node trend aggregation label.

3. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 1, characterized in that: The steps for obtaining the load disturbance section sequence are specifically as follows: S101: Collect power data from consecutive time slices of energy storage access nodes in the distribution network, detect the power value between two adjacent time slices, and construct a power change direction sequence based on the difference in the power values. Select points where the change direction changes from positive to negative or from negative to positive as direction reversal points, extract them as mutation candidate nodes, and generate a mutation candidate node sequence. S102: Based on the node position in the mutation candidate node sequence, the power values of the two time slices before and after the node are called, and the forward change amount and the backward change amount are respectively calculated. The two are compared with the node change direction, and the node whose change amplitude direction is consistent with the original change direction is determined as the starting node for identifying the reference direction, thereby obtaining a direction reference positioning node set; S103: Locate the node set based on the direction reference, detect whether the power change direction of the adjacent nodes is continuously reversed, and locate the node as the disturbance end node if the number of continuous reversals exceeds the set number of reversals. Mark the time slice between the start node and the disturbance end node to obtain the load disturbance segment sequence.

4. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 3, characterized in that: The steps for obtaining the energy storage unit list are specifically as follows: S201: Extracting state of charge records of energy storage access nodes in the distribution network within a corresponding time period based on the intervals marked by the load disturbance segment sequence, arranging the state of charge values of the nodes in chronological order, and obtaining state of charge trend data of the energy storage nodes; S202: Retrieving the state of charge values of adjacent cycles in the state of charge trend data of the energy storage node, sequentially comparing each set of values, recording change direction marks and connecting them to form a trend change sequence, identifying segments where the state of charge values continuously change, and obtaining a time slice set of the rising trend segment; S203: Filter the energy storage access node numbers with an increasing state of charge trend according to the time position corresponding to the increasing trend segment time slice set, sort out the nodes that meet the requirements, remove duplicates and aggregate them, and generate an energy storage unit list.

5. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 1, characterized in that: The steps for obtaining the intervention node trend set are specifically as follows: S401: Based on the timing information corresponding to the node paths in the energy storage path map and the real-time voltage values of the nodes in the time period, the ratio of the voltage value difference in consecutive time slices to the time interval is calculated, and the ratio is combined with the voltage change direction of the adjacent time periods to construct a main vector to obtain a node main vector set; S402: Based on the current release direction and charge state change direction data of the nodes in the node main vector set within the same time period, an auxiliary vector is constructed to screen nodes that simultaneously meet the requirements of voltage rate being in an increasing state, voltage value being in a continuously decreasing section, and current continuous release direction, to generate an intervention node trend set.

6. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 5, characterized in that: The ratio of the voltage difference to the time interval in the continuous time slices is calculated using the formula: ； in, Representative Node With node The ratio of the voltage difference within the time period to the time interval, and Represents nodes and nodes At the time point and The voltage value, Representative Node With node The time interval between Representative The direction of voltage change within a period of time, Represents the total number of voltage change directions.

7. The method for optimizing energy storage scheduling in a distribution network based on dynamic load forecasting according to claim 1, characterized in that: The method further comprises step S5: S5: calling the intervention node trend set, setting release change limits according to the release curve adjustment standard, performing amplitude limiting processing on the current release rate, and performing a scheduling behavior direction locking operation on the energy storage units in the energy storage path that are connected to the node, thereby generating an energy storage release freeze instruction set; The energy storage release freezing instruction set includes a release rate limit value, a path direction locking number, and a freezing response instruction code.

8. The method for optimizing and dispatching energy storage in a distribution network based on dynamic load forecasting according to claim 7, characterized in that: The steps for obtaining the energy storage release and freeze instruction set are specifically as follows: S501: Based on the intervention node trend set, extract the release rate change value of the node within the time period, call the release change limit interval in the release curve adjustment setting, compare the node change value with the upper and lower limits of the interval, analyze the adjustment range of the release curve by determining whether it exceeds the upper and lower limits, and generate a release change determination trend value; S502: Based on the release change determination trend value, a real-time node release current rate is limited, a release amplitude parameter corresponding to the rate value is extracted, and an interval clipping operation is performed with the boundary value of the release change limit interval, and the release current rate value is updated to generate a node release limit rate; S503: Call the node release limit rate, match the energy storage unit in the energy storage path that is connected to the real-time intervention node, compare the scheduling direction value of the energy storage unit with the direction parameter in the constraint release rate set, lock the scheduling direction and write the direction value into the freeze instruction field, and generate an energy storage release freeze instruction set.

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